Web analytics has evolved through three stages: 1) Server-side measurement of page requests (1995-2002) 2) Client-side tagging of pages (2002-present) 3) Hybrid client-server measurement and tag management (2008-present) Measuring web traffic has transitioned from analyzing server log files to using page tags and tag management systems. Identifying unique users remains a challenge due to factors like cookie deletion and device sharing. Analytics aims to understand user behavior through metrics like pageviews, sessions, and conversion rates across various dimensions like time and content. Optimization testing helps continuously improve performance.

1. Web Analytics Primer
Chad Richeson

2. • Since the dawn of the World Wide Web, site owners have been
trying to quantify Web Traffic.
• Remember when sites reported ‘Hits’?
• Stage 1 (1995-2002):
• Server-side measurement – count the # of server requests
generated
• Stage 2 (2002-Present)
• Client (browser)-side “tagging”
• Stage 3 (2008-Present)
• Client+server hybrid measurement and Tag Management
How To Measure Web Traffic?

3. Web Logs
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Web Logs
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Server-side Measurement
Web
Server
Browser
Browser
Browser
5 Requests
Description
• Browser makes requests of
web server to render page
• Web server “logs” how
many requests were made.
• The birth of log file analysis!
Advantages
• Easy/cheap to implement
• Best option available at the time
Disadvantages
• “Hits” not equivalent to “pages”
• No concept of the User, Visit, etc. at the
log level – must be processed to be
useful
• Also captures non-human traffic
• Content Caching can skew web log
numbers
Web Logs
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4. Server-side Measurement
Log Files:
• Hard to read, but contain rich information
• Data is written to the log in the sequence each event was observed
 Requires Processing to be Useful
• There are a lot of inexpensive tools on the market that will
perform log file analysis (processing) for you, but they can’t
handle large amounts of data or create sophisticated metrics.
• Many companies now generate so much log data that it can’t be
processed by traditional tools – new approaches had to be invented
(more on that later.)

5. What’s Does a Server Log Look Like?
Google crawler
indexing the site
User that came so
the site from
Google
Page view from a
user that was
already on the site
Fields Contained in this Log File:
• IP Address – somewhat useful for IDing users (cookies can also be used but have their own problems.)
• Timestamp – when the event occurred. Beware the timezone of the server!
• Access Request – “GET” me the page using HTTP/1.1 protocol
• Result Status – “200” is success; 404 is Not Found; many other codes exist
• Bytes Transferred – 10801 bytes were transferred in the 2nd log entry
• Referring URL – the previous page the user was on. Not always captured.
• User Agent String – the browser, operating system, & language. Mostly reliable.

6. Client-side Measurement
Web
Server
Browser
Browser
Browser
Description
• Browser makes requests of
web server to render page
• Upon rendering, the page
activates a “tag” .
• The tag sends data to a
measurement server, whose
sole job is to collect and
process web data.
Advantages
• Tags tend to be more reflective of
actual “human” behavior
• Caching not an issue
• Back-end processing is typically
more sophisticated & scalable
• Can have multiple tags per page
• Rich JS information can be collected
Disadvantages
• Each page must be tagged
• Some tags increase Page Load Time
• Cost of tagging and processing (getting
better)
Measure-
ment
System
TAG
TAG
TAG

7. What is a Page Tag?
• A Page Tag is a piece of code that resides in the HTML on the page. It
frequently relies on helper technologies such as Javascript to expand its
capabilities.
• Since a page tag “makes a call”, it CAN slow down how fast your page
loads. Make sure you keep an eye on “PLT” – Page Load Time.
• Large sites typically have many page tags on them:
• Web Analytics (Omniture, WebTrends, CoreMetrics, GA, etc.)
• Custom Targeting (e.g. BlueKai, AudienceManager)
• 3rd Party Ad Networks (e.g. Atlas, DoubleClick, etc.)
• Marketing Campaign Funnel (Ad Networks, Search Engines)
• Testing/Experimentation (Adobe, WebTrends, etc)
• Etc
• Etc

8. What Does a Page Tag Look Like?
Let’s go
Behind
the
Scenes

9. What Does a Page Tag Look Like?

10. What Does a Page Tag Look Like?
Google Analytics tag is
located at the bottom
of the EventBrite page.

11. Client-Server “Hybrid” Measurement
Description
• Web logs and page tracking
are enabled with a common
Page ID
• Data is combined after
collection in a central
storage & processing
environment.
• Best of both worlds?
Advantages
• Useful when the content you’re serving is
different for each customer (only the
server knows the differences)
• Enables “whole page unification” across
Ads & Content
• Very powerful when combined with
Experimentation & Targeting
Disadvantages
• More expensive
• Requires excellent data “mastering”
• Lots of storage needed!
• Lots of data skill needed!
Web
Server
Browser
Browser
Browser
Measure-
ment
System
TAG
TAG
TAG
Web Logs
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Web Logs
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Web Logs
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Integrated
Data Store

12. • Putting a Page Tag on every URL is a tedious job. Many sites
have thousands (or more) URLs.
• Adding, changing, or removing tags typically requires modifying
the HTML of each page… this is time consuming.
• Content Management Systems can streamline some of this, but
do not offer robust Tag Management capabilities.
• If you’re manually editing tags, mistakes can tend to pile up over
time, requiring regular “audits”
• Solution? Centralized Tag Management
Tag Management

13. • Companies such as Adobe and Ensighten offer Centralized Tag
Management Solutions.
• Instead of putting code on the page, the TMS puts a “container” on the
page, which calls the tags you wish to invoke. You manage the tags
from a central server.
• If you have lots of URLs and lots of Tags, leveraging a TMS can drive
tremendous efficiency and fewer mistakes.
Tag Management
Web
Server
Browser
Browser
Browser
Measure-
ment
System
TAG
TAG
TAG
Web Logs
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Web Logs
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Web Logs
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Integrated
Data Store
Tag
Management
System

14. • Identifying “Users” (or Visitors) is one of the biggest challenges
in Web Analytics!
• The Perfect User ID doesn’t yet exist (Facebook notwithstanding)
• Cookies are an old technology but still very prevalent.
• Challenges:
• Users are now using many devices to access the same site (home
computer, work computer, tablet, phone, etc.)
• Sometimes a device is shared across more than one user
(example: a home computer.)
• Unless the user logs in on every device, it’s impossible to know
exactly how many users you have and track them individually.
Cookies & User Identification

15. • What is a cookie?
• A cookie is a small text file that resides in your browser’s cache:
• The first time you visit a web site, it “sets” the cookie. On each
subsequent visit, it “reads” the cookie.
• If you clear your cookies and then visit the site again, the web site
doesn’t recognize you and “sets” a new cookie.
• First party cookie – a cookie that is set & read by the site you are
visiting.
• Third party cookie – a cookie that gets set & read by a third party that
the first party site has included. Sometimes the call to the 3rd party site is
invisible.
Cookies & User Identification
cookie text

16. Cookie Deletion
• Studies have shown that 10-20% of users churn their cookies
frequently – after every session or every day. This churn can lead
to a large number of cookies in your results.
• Example: assume your site has 100 “real” users. During a one
month period…
• 80 of the users will have one cookie each = 80 cookies
• 20 of the users will churn their cookies every day, and create 30
cookies each = 600 cookies.
• Total = 680 cookies, 88% of which look like new users!
Cookies & User Identification

17. Basic Web Metrics
Examples & Definitions
Cookie 001 Cookie 001
Pageview
1
PV 2 PV
3
Day 1 Day 2
Visit (or
Session)
Visit (or
Session)
Visitor (or User)
Pageview
4
PV 5 PV
6
Measures – created by direct
measurement of activity
• Pageviews (count)
• Visitors (count unique)
Calculated Metrics – created by dividing
one measure by another measure
• Pageviews/User
• Pageviews/Session
• Sessions/User
Derived Metrics – created by taking a
metric and a dimension (usually Time),
and creating a new Derived metric.
• Sessions (count)
• Frequency; Days of Use
Indices – created by normalizing a group
of metric observations (usually to 100)
and calculating an observation’s value
relative to the top normalized value.
Example Definitions

18. Measures, Metrics, & Indices
The number of possible Web Metrics is large and increasing. Multiply by
the number of Dimensions, and there is a nearly infinite number of things
and analyst can look at!
• Visitors (Users)
• Customer Sat
• Subscribers
• Pageviews/User
• Sessions/User
• Bounce Rate
• Pageviews
• Transactions
• Subscribers
Measures Metrics Indices
• Visits (Sessions)
• Minutes
• Days of Use
Common Dimensions
Time
Geography
Product
Customer
Customer Segment
Content Type
Channel
Transaction Type
Campaign
Sales Owner
Custom Events
Product
Customer
Brand &
Campaign
• Top 10% Pages
• Engagement
• Net Promoter
Monetization
• Top 10% Users
• Engagement
• Customer Value
• % Logged In
• Impressions
• Revenue
• Click Position
• Rev/User (ARPU)
• Rev/kPV
• CPM
• % Heavy Users
• Retention
• % of Visits
• Share
• Reach
• Impressions
• Clicks
• Acquisitions
• Click-thru Rate
• Cost/Acquisition
• Campaign ROI
• Top Campaigns
• Price per Click
• Ad Market Share
• Rev/Advertiser
• Top 10% Pages
• Top Advertisers

19. • Prioritize. Make sure your business leaders have designated their
top metrics (commonly called KPIs – Key Performance Indicators)
• See books by Eric T. Peterson for more help here.
• Build a Tree that shows how all of your metrics interrelate.
Connect them to revenue and profit. Focus on the DRIVERS and
LEADING INDICATORS.
• Revenue is a Lagging Indicator!
• Customer Satisfaction is a Leading Indicator.
• New Customers is a Driver.
• Useful Technique with Web Data: DE-AVERAGE
How to Cope?

20. The Benefits of De-Averaging
10%
1%
10%
2%
10%
4%
10%
4%
10%
5%
10%
8%
10%
9%
10%
10%
10%
20%
10%
40%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Users Pages
30%
40%
11%
100% 100%
Top
Decile
Bottom
Decile
Source: compilation of various analyses
70%
• There really is no
“average” Internet
user.
• Small numbers of
users can drive a
lot of traffic.
• Break down your
important metrics
into deciles to gain
new levels of
insight.

21. Days of Use
Current
Year
Actual
Next
Year
Goal % gro
>8 (Light) 7m 6m (15%)
8-21 (Med) 3m 4m 33%
>22 (Heavy) 1m 2m 100%
Total 11m 12m 10%
Using De-Averaging to Create New Metrics
Threshold metrics add a “mix” focus to business goals, which
can have major impact on growth.
-
5.0
10.0
15.0
20.0
25.0
30.0
35.0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
8.0 days of
use
Visitors by Days of Use
22.0 days
of use
# low
users
# medium
users
# high users
MED+HIGH
total users
HIGH
users
Setting goals on Threshold metrics allows
you to change the mix of users, while also
growing & migrating the user base upward.
Assuming flat PV/user, changing the mix
of the user base would increase
pageviews by 50%, while total users only
grow by 10%.
Applying to Business Goals

22. The Law of “De-Averages”
Be suspicious of averages – taking them at face value can
sometimes lead to wrong conclusions.
1. Always understand how a metric will be used, and whether a average (mean,
median, or mode) is the best way to present the metric.
2. Always make an attempt to understand the shape of the distribution behind the
metric’s average, and if that distribution is changing over time.
3. Where possible, break a metric out into quintiles or deciles, based on frequency
of observations or based on users. Map the deciles to usage, to create a
customized version of the “80/20” rule for that metric.
4. If the story of the de-average is significantly different than the story of the
average, ensure business owners and other action drivers are sufficiently
educated.
5. Where needed, set primary or secondary business goals on threshold metrics and
specific segments, to further drive desired performance improvements.

23. Generic Purchase Funnel
Awareness
Consideration
Conversion
• Most purchase funnels
consist of three main parts.
• Web analytics can be used to
optimize and improve each
step!

24. Your Site
An Online Purchase Funnel
Site Visit
#1
Site Visit
#2
Purchase
Landing
Page
Display Ads
& Rich
Media
Paid Search
& Content
Ads
Mobile
Ads*
E-Mail
Marketing
Direct Navigation
“Conversion”
Day 1 Days 2-7 Days 7+
Ad Impression Landing Page Site Visits
Optimize!
Ad Networks
Awareness Consideration Conversion
Optimize!Optimize! Target! Target!

25. How to improve an Online Purchase Funnel:
• Create multiple (even targeted) landing pages. The language on your
landing page needs to match the Display Ad, Email, or Keyword that
brought the user there!
• Example: if a user searched on ‘Shoes’ and clicked on your ad, the landing
page should be the Shoes section, not your general storefront.
• Improving stick rates early in the funnel lead to better performance
later in the funnel.
• Understand the user “paths” that lead to conversion. Start with what’s
working, and decompose it into high conversion paths and low
conversion paths. Test & iterate.
• Constantly test (optimize) your checkout/conversion page(s). This is a
very important page – small improvements can mean a lot of money.
• Targeting can improve performance at every stage.
Improving Your Purchase Funnel

26. Optimization is the practice of using hypothesis testing and
experimentation to continuously improve site and campaign
performance.
 Tactics include A/B and Multivariate Testing
Continuous Improvement methodology
Benefits
Sustained performance improvement
Predictable, controlled routine
Lower risk from changes
Works well with Targeting
Optimization

27. A
Did “Mobile” or “Online” get more pre-header clicks?
B
“Mobile”
“Online”
Which Test Won???
Optimization Examples

28. A
Did “Mobile” or “Online” get more pre-header clicks?
“Mobile”
Version A received a 173% increase in unique clicks
Test was conducted after learning 25% of members
read email on their mobile devices
Optimization Examples

29. Which Test Won???
A
Do we get more leads with sub-navigation?
BWithout Sub-Navigation With Sub-Navigation
Optimization Examples

30. Do we get more leads with sub-navigation?
B With Sub-Navigation
Version B increased leads by
39%.
Dell noticed a correlation
between leads and visitors
seeing 5 specific pages on the
site.
Good site analytics leads to
strong hypotheses!
Optimization Examples

31. A
B
Sarah
Blair
A
Do people like Sarah or Blair better?
(which image increased account sign-ups?)
Optimization Examples

32. A
Sarah
A
• Sarah drove 36% more account signups than Blair
Do people like Sarah or Blair better?
(which image increased account sign-ups?)
Optimization Examples

33. • Optimization should be an ongoing program – you will continue
to get benefit as long as you do it.
• As you get more sophisticated, you can run many experiments at
once. But don’t get sloppy with the samples 
• Be patient – some tests (especially UX changes) take many
months to show results.
• Get agreement up front on the Evaluation Criteria with Business
and IT decision-makers.
• Be aware of confounding factors such as site changes that occur
independently of your experiments. They can pollute your
metrics and lead to incorrect conclusions.
• Targeting increases the power of Optimization.
Optimization Tips

34. • Bring data sources together to drive even more value.
• Connect as much data as possible to the Person level.
• Web & Mobile experiences offer a trove of useful data.
• Exercise this power in a way that improves the customer
experience without violating privacy standards.
Putting It All Together!
Demographics Technographics Preferences Current Intent Current Location
Age
Gender
Zip
Education
Income
Device
Operating System
Screen Size
Bandwidth
Hobbies
Lifestyle
Life Stage
Recent Transactions
Recent Views
Queries
Time of Day
Current Location
Speed
Direction

35. • Some customer attributes change very slowly (e.g. Demographics)
• Some attributes change very quickly (e.g. Current Location)
The Current Frontier:
• Algorithms that map data to the “Person”
• Company-wide data integration (Big Data!)
• Designing analytics systems to take advantage of fast-changing customer
attributes.
• Web, Phone, and Tablet experiences that all recognize you as the same
person.
• High-end visualization & segmentation tools.
 Leveraging all of these capabilities to PREDICT what the customer
wants next.
This is the Frontier!

36. Living the Dream (Predictive Analytics)
Moving from Mass Marketing to
Predictive Marketing
“Every Customer A Segment”
3 - Prediction
• Gather & monitor customer
context
• Evaluate explicit customer
signals in the correct context
• Generate predictions of what
the customer is most likely to
need or do next
• Rapidly test and iterate
• Apply learning from each
customer to next customer in
near-realtime
LEAP
“10x ROI”
“Cast Different Nets”
2 - SegmentationSTEP
• Analyze historical customer
data to determine segments
• Create a strategy for each
segment, and goals to move
customers between segments
• Generate different messaging
for each segment
• Review performance and re-
craft messages, or re-segment
customer base
“3x ROI”
“Spray & Pray”
1 - Mass Market
• Perform market research from
sample of customers to
determine needs
• Create marketing messaging
that addresses the most
common needs
• Review performance and
adjust messaging, in context of
newest research
“1x ROI”

37. • To understand Web Analytics, you have to understand how the
data is generated. But don’t get lost among the trees.
• User Identification is an ongoing challenge and should be seen as
an area that will require mental bandwidth, creativity, and
innovation to get right.
• Don’t take Web Metrics at face value. Get nimble at de-
composing them and understanding the underlying trends.
• Optimization (A/B testing) programs take time to develop, but
pay off handsomely in the long run. Start learning sooner than
later.
• The grand prize when you put everything together is multi-
channel, predictive, customer-level analytics. This is the future of
business, not just analytics!
In Summary